Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/129731
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Type: | Conference paper |
Title: | Medical data inquiry using a question answering model |
Author: | Liao, Z. Liu, L. Wu, Q. Teney, D. Shen, C. Van Den Hengel, A. Verjans, J. |
Citation: | Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging, 2020, vol.2020-April, pp.1490-1493 |
Publisher: | IEEE |
Publisher Place: | online |
Issue Date: | 2020 |
Series/Report no.: | IEEE International Symposium on Biomedical Imaging |
ISBN: | 9781538693308 |
ISSN: | 1945-7928 1945-8452 |
Conference Name: | IEEE International Symposium on Biomedical Imaging (ISBI) (3 Apr 2020 - 7 Apr 2020 : Iowa City, USA) |
Statement of Responsibility: | Zhibin Liao, Lingqiao Liu, Qi Wu, Damien Teney, Chunhua Shen, Anton van den Hengel, Johan Verjans |
Abstract: | Access to hospital data is commonly a difficult, costly and time-consuming process requiring extensive interaction with network administrators. This leads to possible delays in obtaining insights from data, such as diagnosis or other clinical outcomes. Healthcare administrators, medical practitioners, researchers and patients could benefit from a system that could extract relevant information from healthcare data in real-time. In this paper, we present a question answering system that allows health professionals to interact with a large-scale database by asking questions in natural language. This system is built upon the BERT and SQLOVA models, which translate a user's request into an SQL query, which is then passed to the data server to retrieve relevant information. We also propose a deep bilinear similarity model to improve the generated SQL queries by better matching terms in the user's query with the database schema and contents. This system was trained with only 75 real questions and 455 back-translated questions, and was evaluated over 75 additional real questions about a real health information database, achieving a retrieval accuracy of 78%. |
Rights: | ©2020 IEEE |
DOI: | 10.1109/ISBI45749.2020.9098531 |
Published version: | http://dx.doi.org/10.1109/isbi45749.2020.9098531 |
Appears in Collections: | Aurora harvest 8 Australian Institute for Machine Learning publications |
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